Introduction

• Air pollution can be detrimental to both our health and the climate
  • Outdoor and indoor air pollution cause chronic pain, respiratory diseases, shortened lifespan
  • Air pollution kills about 7 million people worldwide every year
  • Hopefully this information can showcase the importance of air pollution and that we should be more mindful about our planet
• Overview
  • We will see how different air pollution types affect the population
  • compare past and present population numbers
  • determine which air pollutant type has the highest associated death rate

Packages Required

#This will allow us to filter through our data 
library(tidyverse)
library(dplyr)
#This will help us plot figures to showcase our findings
library(ggplot2)
#This will help us organize and display our data as necessary 
library(knitr)
library(kableExtra)
#This expands our plot uses 
library(plotly)
#Scientific Notation Disabled 
options(scipen=999)

Deaths Data

• Deaths from pollution:
  • Kaggle: [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017]
  • Author: Akshat Giri
  • Last update: 2 years ago

Import the deaths-due-to-air-pollution data

deaths_df <- data.frame(read.csv("death-rates-from-air-pollution.csv"))

We are going to rename a few of the columns and glimpse the data

colnames(deaths_df) <- c("country", "acronym", "year", "total_deaths", "indoor_deaths", "outdoor_deaths", "ozone_deaths")

glimpse(deaths_df)

## Rows: 6,468
## Columns: 7
## $ country        <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist~
## $ acronym        <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG",~
## $ year           <int> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1~
## $ total_deaths   <dbl> 299.4773, 291.2780, 278.9631, 278.7908, 287.1629, 288.0~
## $ indoor_deaths  <dbl> 250.3629, 242.5751, 232.0439, 231.6481, 238.8372, 239.9~
## $ outdoor_deaths <dbl> 46.44659, 46.03384, 44.24377, 44.44015, 45.59433, 45.36~
## $ ozone_deaths   <dbl> 5.616442, 5.603960, 5.611822, 5.655266, 5.718922, 5.739~

Data Variables

Variables that interest us here include:

• country
• total_deaths: per 100,000
• indoor_deaths: Indoor air pollution is considered pollution that occurs in the household. Cooking with solid fuels:
  • Wood
  • Crop waste, dung
  • Charcoal, coal
• outdoor_deaths: Outdoor air or ambient air are emissions caused by combustion processes from motor vehicles, solid fuel burning and industry
  • Ozone (O3)
  • Particulate matter (PM10 and PM2.5)
  • Nitrogen dioxide (NO2)
  • Carbon monoxide (CO)
  • Sulfur dioxide (SO2)
• ozone_deaths: Ozone is a gas that occurs both in Earth’s upper atmosphere and at ground level. Ozone in the atmosphere is an important and helpful greenhouse gas, but ground-level ozone is created by extensive use of fossil fuels:
  • Pollutants emitted by cars
  • Power plants, industrial boilers, refineries, chemical plants

World Population Data

• World Population:
  • Kaggle: [https://www.kaggle.com/datasets/imdevskp/world-population-19602018]
  • Author: Devakumar K. P.
  • Last update: 2 Years ago

Now, let’s take a look at the population data.

world_pop <- read.csv("population_total_long.csv")
glimpse(world_pop)

## Rows: 12,595
## Columns: 3
## $ Country.Name <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "~
## $ Year         <int> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 196~
## $ Count        <int> 54211, 8996973, 5454933, 1608800, 13411, 92418, 20481779,~

To get a general idea of ‘deaths-dataframe’ we made, let’s make a plots to see what’s happening. This is a plot of indoor x outdoor deaths around the world by country.

This is a mess, and so we chose two countries from each continent (a high-population and a low-population country) to graph.

We selected a high population from each continent and used the formula below to determine the low population.

Low population = high population * .10

Combine Data Sets

Death Count

Which country has the highest death count?

Let’s make a table depicting the high and low populated countries and their respected death count due to pollution.

Here’s a graph to clearly visualize the previous table

So we’ve looked at the deaths due to pollution, but what percentage of the population was affected?

Pollution Types

Which type of pollution has the greatest number of deaths?

## # A tibble: 6 x 4
##   country       avg_indoor avg_outdoor avg_ozone
##   <chr>              <dbl>       <dbl>     <dbl>
## 1 Australia          0.249        17.2     0.360
## 2 Brazil            19.4          26.8     2.74 
## 3 Germany            0.717        25.5     2.34 
## 4 Nigeria           75.9          35.2     2.12 
## 5 Pakistan          87.7          50.5    10.4  
## 6 United States      0.166        22.8     3.92

## # A tibble: 6 x 4
##   country     avg_indoor avg_outdoor avg_ozone
##   <chr>            <dbl>       <dbl>     <dbl>
## 1 Canada          0.0651        16.4    1.97  
## 2 Chile           8.69          27.2    0.850 
## 3 Malawi        132.            13.8    3.39  
## 4 New Zealand     0.291         15.6    0.0728
## 5 Serbia         35.9           42.7    2.94  
## 6 Sri Lanka      44.5           24.8    0.430

Pollution Over Time

Let’s look at the previous two decades and compare the death count Has there been a change?

Let’s graph the previous tables!

The first decade.

This shows the second decade.

Which year had the worst indoor? Outdoor particulate? Outdoor ozone?

Indoor Deaths

Outdoor Deaths

Ozone Deaths

Which is worse?

outdoor or indoor pollution?

Let’s reintroduce a graph we looked at earlier. Instead this time we will combine the pollutant types together.

We cannot conclude which is worse.

• Low Populated Countries:
  • it’s half-and-half
• High Populated Countries:
  • Outdoor pollution seems to be more detrimental with the exception of two countries in this sample set.

We have this included already

#Mean total deaths from 1996-2017 of high-population countries
deaths_highpop_countries <- deaths_df %>% 
  filter(country %in% c('United States', 'Brazil', 'Nigeria', 'Germany', 'Pakistan', 'Australia')) %>% 
  group_by(country) %>% 
  select(total_deaths) %>% 
  summarize(average_death_high = mean(total_deaths))

## Adding missing grouping variables: `country`

#Mean total deaths from 1990-2017 of high-population countries
deaths_lowpop_countries<- deaths_df %>% 
  filter(country %in% c('Canada', 'Chile', 'Malawi', 'Serbia', 'Sri Lanka', 'New Zealand')) %>% 
  group_by(country) %>% 
  select(total_deaths) %>% 
  summarize(average_death_low = mean(total_deaths))

## Adding missing grouping variables: `country`

#Average Death of both High and Low Populated countries  
kable(list(deaths_highpop_countries, deaths_lowpop_countries))

ggplot(deaths_highpop_countries)+
  geom_col(mapping = aes(x=country, y=average_death_high))+
             xlab("Country")+
             ylab("Average deaths (per 100,000)")+
             ggtitle("Average total deaths in high-population countries")+
  coord_flip()

ggplot(deaths_lowpop_countries)+
  geom_col(mapping = aes(x=country, y=average_death_low))+
             xlab("Country")+
             ylab("Average deaths (per 100,000)")+
             ggtitle("Average total deaths in low-population countries")+
  coord_flip()

Summary

Sources